Reallocating storage in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) execution unit includes updating a plurality of weighting factors corresponding to each of a plurality of memories in response to an indication of a change in memory capacity of one of the plurality of memories. At least one encoded data slice is received for storage by the DST execution unit. A plurality of scores are generated corresponding to each of the plurality of memories, wherein each of the plurality of scores is based on one of the plurality of weighting factors of a corresponding one of the plurality of memories. One of the plurality of memories is selected based on the plurality of scores, and the at least one encoded data slice is stored in the selected memory.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

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BACKGROUND OF THE INVENTION

Technical Field of the Invention

Aspects of this invention relate generally to computer networks and moreparticularly to dispersed storage of data and distributed taskprocessing of data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system in accordance with the present invention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 4A is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention;

FIG. 4B is a flowchart illustrating an example of selecting the resourcein accordance with the present invention;

FIG. 4C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention;

FIG. 4D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory in accordance with the present invention;

FIG. 5 is schematic block diagram of an embodiment of a dispersedstorage network (DSN) in accordance with the present invention; and

FIG. 6 is a flowchart illustrating an example of reallocating encodeddata slice storage in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a distributedcomputing system 10 that includes a user device 12 and/or a user device14, a distributed storage and/or task (DST) processing unit 16, adistributed storage and/or task network (DSTN) managing unit 18, a DSTintegrity processing unit 20, and a distributed storage and/or tasknetwork (DSTN) module 22. The components of the distributed computingsystem 10 are coupled via a network 24, which may include one or morewireless and/or wire lined communication systems; one or more non-publicintranet systems and/or public internet systems; and/or one or morelocal area networks (LAN) and/or wide area networks (WAN). Hereafter,the distributed computing system 10 may be interchangeably referred toas a dispersed storage network (DSN).

The DSTN module 22 includes a plurality of distributed storage and/ortask (DST) execution units 36 that may be located at geographicallydifferent sites (e.g., one in Chicago, one in Milwaukee, etc.). Each ofthe DST execution units is operable to store dispersed error encodeddata and/or to execute, in a distributed manner, one or more tasks ondata. The tasks may be a simple function (e.g., a mathematical function,a logic function, an identify function, a find function, a search enginefunction, a replace function, etc.), a complex function (e.g.,compression, human and/or computer language translation, text-to-voiceconversion, voice-to-text conversion, etc.), multiple simple and/orcomplex functions, one or more algorithms, one or more applications,etc. Hereafter, the DST execution unit may be interchangeably referredto as a storage unit and a set of DST execution units may beinterchangeably referred to as a set of storage units.

Each of the user devices 12-14, the DST processing unit 16, the DSTNmanaging unit 18, and the DST integrity processing unit 20 include acomputing core 26 and may be a portable computing device and/or a fixedcomputing device. A portable computing device may be a social networkingdevice, a gaming device, a cell phone, a smart phone, a digitalassistant, a digital music player, a digital video player, a laptopcomputer, a handheld computer, a tablet, a video game controller, and/orany other portable device that includes a computing core. A fixedcomputing device may be a personal computer (PC), a computer server, acable set-top box, a satellite receiver, a television set, a printer, afax machine, home entertainment equipment, a video game console, and/orany type of home or office computing equipment. User device 12 and DSTprocessing unit 16 are configured to include a DST client module 34.

With respect to interfaces, each interface 30, 32, and 33 includessoftware and/or hardware to support one or more communication links viathe network 24 indirectly and/or directly. For example, interface 30supports a communication link (e.g., wired, wireless, direct, via a LAN,via the network 24, etc.) between user device 14 and the DST processingunit 16. As another example, interface 32 supports communication links(e.g., a wired connection, a wireless connection, a LAN connection,and/or any other type of connection to/from the network 24) between userdevice 12 and the DSTN module 22 and between the DST processing unit 16and the DSTN module 22. As yet another example, interface 33 supports acommunication link for each of the DSTN managing unit 18 and DSTintegrity processing unit 20 to the network 24.

The distributed computing system 10 is operable to support dispersedstorage (DS) error encoded data storage and retrieval, to supportdistributed task processing on received data, and/or to supportdistributed task processing on stored data. In general and with respectto DS error encoded data storage and retrieval, the distributedcomputing system 10 supports three primary operations: storagemanagement, data storage and retrieval, and data storage integrityverification. In accordance with these three primary functions, data canbe encoded (e.g., utilizing an information dispersal algorithm (IDA),utilizing a dispersed storage error encoding process), distributedlystored in physically different locations, and subsequently retrieved ina reliable and secure manner. Hereafter, distributedly stored may beinterchangeably referred to as dispersed stored. Such a system istolerant of a significant number of failures (e.g., up to a failurelevel, which may be greater than or equal to a pillar width (e.g., anIDA width of the IDA) minus a decode threshold minus one) that mayresult from individual storage device (e.g., DST execution unit 36)failures and/or network equipment failures without loss of data andwithout the need for a redundant or backup copy. Further, thedistributed computing system 10 allows the data to be stored for anindefinite period of time without data loss and does so in a securemanner (e.g., the system is very resistant to unauthorized attempts ataccessing the data).

The second primary function (i.e., distributed data storage andretrieval) begins and ends with a user device 12-14. For instance, if asecond type of user device 14 has data 40 to store in the DSTN module22, it sends the data 40 to the DST processing unit 16 via its interface30. The interface 30 functions to mimic a conventional operating system(OS) file system interface (e.g., network file system (NFS), flash filesystem (FFS), disk file system (DFS), file transfer protocol (FTP),web-based distributed authoring and versioning (WebDAV), etc.) and/or ablock memory interface (e.g., small computer system interface (SCSI),internet small computer system interface (iSCSI), etc.). In addition,the interface 30 may attach a user identification code (ID) to the data40.

To support storage management, the DSTN managing unit 18 performs DSmanagement services. One such DS management service includes the DSTNmanaging unit 18 establishing distributed data storage parameters (e.g.,vault creation, distributed storage parameters, security parameters,billing information, user profile information, etc.) for a user device12-14 individually or as part of a group of user devices. For example,the DSTN managing unit 18 coordinates creation of a vault (e.g., avirtual memory block associated with a portion of an overall namespaceof the DSN) within memory of the DSTN module 22 for a user device, agroup of devices, or for public access and establishes per vaultdispersed storage (DS) error encoding parameters for a vault. The DSTNmanaging unit 18 may facilitate storage of DS error encoding parametersfor each vault of a plurality of vaults by updating registry informationfor the distributed computing system 10. The facilitating includesstoring updated system registry information in one or more of the DSTNmodule 22, the user device 12, the DST processing unit 16, and the DSTintegrity processing unit 20.

The DS error encoding parameters (e.g., or dispersed storage errorcoding parameters for encoding and decoding) include data segmentinginformation (e.g., how many segments data (e.g., a file, a group offiles, a data block, etc.) is divided into), segment securityinformation (e.g., per segment encryption, compression, integritychecksum, etc.), error coding information (e.g., pillar/IDA width,decode threshold, read threshold, write threshold, etc.), slicinginformation (e.g., the number of encoded data slices that will becreated for each data segment); and slice security information (e.g.,per encoded data slice encryption, compression, integrity checksum,etc.).

The DSTN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSTN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSTN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSTN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSTNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

Another DS management service includes the DSTN managing unit 18performing network operations, network administration, and/or networkmaintenance. Network operations includes authenticating user dataallocation requests (e.g., read and/or write requests), managingcreation of vaults, establishing authentication credentials for userdevices, adding/deleting components (e.g., user devices, DST executionunits, and/or DST processing units) from the distributed computingsystem 10, and/or establishing authentication credentials for DSTexecution units 36. Network administration includes monitoring devicesand/or units for failures, maintaining vault information, determiningdevice and/or unit activation status, determining device and/or unitloading, and/or determining any other system level operation thataffects the performance level of the system 10. Network maintenanceincludes facilitating replacing, upgrading, repairing, and/or expandinga device and/or unit of the system 10.

To support data storage integrity verification within the distributedcomputing system 10, the DST integrity processing unit 20 performsrebuilding of ‘bad’ or missing encoded data slices. At a high level, theDST integrity processing unit 20 performs rebuilding by periodicallyattempting to retrieve/list encoded data slices, and/or slice names ofthe encoded data slices, from the DSTN module 22. For retrieved encodedslices, they are checked for errors due to data corruption, outdatedversion, etc. If a slice includes an error, it is flagged as a ‘bad’slice. For encoded data slices that were not received and/or not listed,they are flagged as missing slices. Bad and/or missing slices aresubsequently rebuilt using other retrieved encoded data slices that aredeemed to be good slices to produce rebuilt slices. The rebuilt slicesare stored in memory of the DSTN module 22. Note that the DST integrityprocessing unit 20 may be a separate unit as shown, it may be includedin the DSTN module 22, it may be included in the DST processing unit 16,and/or distributed among the DST execution units 36.

Each slice name is unique to a corresponding encoded data slice andincludes multiple fields associated with the overall namespace of theDSN. For example, the fields may include a pillar number/pillar index, avault identifier, an object number uniquely associated with a particularfile for storage, and a data segment identifier of a plurality of datasegments, where the particular file is divided into the plurality ofdata segments. For example, each slice name of a set of slice namescorresponding to a set of encoded data slices that has been dispersedstorage error encoded from a common data segment varies only by entriesof the pillar number field as each share a common vault identifier, acommon object number, and a common data segment identifier.

To support distributed task processing on received data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task processing) management and DST execution on received data.With respect to the storage portion of the DST management, the DSTNmanaging unit 18 functions as previously described. With respect to thetasking processing of the DST management, the DSTN managing unit 18performs distributed task processing (DTP) management services. One suchDTP management service includes the DSTN managing unit 18 establishingDTP parameters (e.g., user-vault affiliation information, billinginformation, user-task information, etc.) for a user device 12-14individually or as part of a group of user devices.

Another DTP management service includes the DSTN managing unit 18performing DTP network operations, network administration (which isessentially the same as described above), and/or network maintenance(which is essentially the same as described above). Network operationsinclude, but are not limited to, authenticating user task processingrequests (e.g., valid request, valid user, etc.), authenticating resultsand/or partial results, establishing DTP authentication credentials foruser devices, adding/deleting components (e.g., user devices, DSTexecution units, and/or DST processing units) from the distributedcomputing system, and/or establishing DTP authentication credentials forDST execution units.

To support distributed task processing on stored data, the distributedcomputing system 10 has two primary operations: DST (distributed storageand/or task) management and DST execution on stored data. With respectto the DST execution on stored data, if the second type of user device14 has a task request 38 for execution by the DSTN module 22, it sendsthe task request 38 to the DST processing unit 16 via its interface 30.With respect to the DST management, it is substantially similar to theDST management to support distributed task processing on received data.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSTN interface module 76.

The DSTN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). TheDSTN interface module 76 and/or the network interface module 70 mayfunction as the interface 30 of the user device 14 of FIG. 1. Furthernote that the IO device interface module 62 and/or the memory interfacemodules may be collectively or individually referred to as IO ports.

FIG. 3 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 1-n that includes two or more DST execution units 36 ofFIG. 1 (which form at least a portion of DSTN module 22 of FIG. 1), aDST managing module (not shown), and a DST integrity verification module(not shown). The DST client module 34 includes an outbound DSTprocessing section 80 and an inbound DST processing section 82. Each ofthe DST execution units 1-n includes a controller 86, a processingmodule 84, memory 88, a DT (distributed task) execution module 90, and aDST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92can be of any size and of any content, where, due to the size (e.g.,greater than a few Terabytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the data 92 can be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terabytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error can encode the data partition to produce encoded data slicesand groups the encoded data slices into a slice grouping 96. Inaddition, the outbound DST processing section 80 can partition the task94 into partial tasks 98, where the number of partial tasks 98 maycorrespond to the number of slice groupings 96.

The outbound DST processing section 80 can then send, via the network24, the slice groupings 96 and the partial tasks 98 to the DST executionunits 1-n of the DSTN module 22 of FIG. 1. For example, the outbound DSTprocessing section 80 sends slice group 1 and partial task 1 to DSTexecution unit 1. As another example, the outbound DST processingsection 80 can send slice group #n and partial task #n to DST executionunit #n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit1 performs partial task 1 on slice group 1 to produce a partial result1, for results. As a more specific example, slice group 1 corresponds toa data partition of a series of digital books and the partial task #1corresponds to searching for specific phrases, recording where thephrase is found, and establishing a phrase count. In this more specificexample, the partial result 1 includes information as to where thephrase was found and includes the phrase count.

Upon completion of generating their respective partial results 102, theDST execution units can send, via the network 24, their partial results102 to the inbound DST processing section 82 of the DST client module34. The inbound DST processing section 82 can process the receivedpartial results 102 to produce a result 104. Continuing with thespecific example of the preceding paragraph, the inbound DST processingsection 82 can combine the phrase count from each of the DST executionunits 36 to produce a total phrase count. In addition, the inbound DSTprocessing section 82 can combine the ‘where the phrase was found’information from each of the DST execution units 36 within theirrespective data partitions to produce ‘where the phrase was found’information for the series of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units 36(e.g., memory of the DSTN module). In this example, the task 94 isretrieve data stored in the memory of the DSTN module. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 1-n.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 36 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit 1 receives partialtask 1 and retrieves, in response thereto, retrieved slices 1. The DSTexecution units 36 send their respective retrieved slices 100 to theinbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 can then DS errordecodes the encoded slices per data partition to produce datapartitions. The inbound DST processing section 82 can de-partition thedata partitions to recapture the data 92.

FIG. 4A is a schematic block diagram of an embodiment of a decentralizedagreement module 350 that includes a set of deterministic functions 1-N,a set of normalizing functions 1-N, a set of scoring functions 1-N, anda ranking function 352. The deterministic function, the normalizingfunction, the scoring function, and/or the ranking function 352, can beimplemented utilizing the processing module 84 of FIG. 3. Thedecentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module is implemented utilizing thedistributed storage and task (DST) client module 34 of FIG. 1 via aprocessing system that includes at least one processor and memory thatstores instruction that configure the processor or processors to performthe functions described herein.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354and/or other information. The ranked scoring information request 354 caninclude an asset identifier (ID) 356 of an asset associated with therequest, an asset type indicator, one or more location identifiers oflocations associated with the DSN, one or more corresponding locationweights a requesting entity ID and/or other information. The asset caninclude any portion of data associated with the DSN including one ormore asset types including a data object, a data record, an encoded dataslice, a data segment, a set of encoded data slices, a plurality of setsof encoded data slices and/or other data. As such, the asset ID 356 ofthe asset can include a data name, a data record identifier, a sourcename, a slice name, a plurality of sets of slice names and/or otherinformation.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations include a storage unit, a memory device of the storageunit, a site, a storage pool of storage units, a pillar index associatedwith each encoded data slice of a set of encoded data slices generatedby an information dispersal algorithm (IDA), a DST client module 34 ofFIG. 1, a DST processing unit 16 of FIG. 1, a DST integrity processingunit 20 of FIG. 1, a DSTN managing unit 18 of FIG. 1, a user device 12of FIG. 1, and a user device 14 of FIG. 1.

Each location is associated with a location weight based on a resourceprioritization of utilization scheme and/or a physical configuration ofthe DSN. The location weight can include an arbitrary bias which adjustsa proportion of selections to an associated location such that aprobability that an asset will be mapped to that location is equal tothe location weight divided by a sum of all location weights for alllocations of comparison. For example, each storage pool of a pluralityof storage pools can be associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacitycan be associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information can include location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction can include a hashing function, a hash-based messageauthentication code function, a mask generating function, a cyclicredundancy code function, hashing module of a number of locations,consistent hashing, rendezvous hashing, a sponge function and/or someother function. As a specific example, deterministic function 2 appendsa location ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function can include dividing the interim result by a numberof possible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. Performance of the scoring functioncan include dividing an associated location weight by a negative log ofthe normalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function can include rank ordering eachscore with other scores of the set of scores 1-N, where a highest scoreis ranked first or highest. As such, a location associated with thehighest score may be considered a highest priority location for resourceutilization (e.g., accessing, storing, retrieving, etc. the given assetof the request). Having generated the ranked scoring information 358,the decentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

FIG. 4B is a flowchart illustrating an example of selecting a resource.In step 360, a processing module (e.g., of a decentralized agreementmodule) receives a ranked scoring information request from a requestingentity with regards to a set of candidate resources. For each candidateresource, the method continues at step 362 where the processing moduleperforms a deterministic function on a location identifier (ID) of thecandidate resource and an asset ID of the ranked scoring informationrequest to produce an interim result. As a specific example, theprocessing module combines the asset ID and the location ID of thecandidate resource to produce a combined value and performs a hashingfunction on the combined value to produce the interim result.

For each interim result, the method continues at step 364 where theprocessing module performs a normalizing function on the interim resultto produce a normalized interim result. As a specific example, theprocessing module obtains a permutation value associated with thedeterministic function (e.g., maximum number of permutations of outputof the deterministic function) and divides the interim result by thepermutation value to produce the normalized interim result (e.g., with avalue between 0 and 1).

For each normalized interim result, the method continues at step 366where the processing module performs a scoring function on thenormalized interim result utilizing a location weight associated withthe candidate resource associated with the interim result to produce ascore of a set of scores. As a specific example, the processing moduledivides the location weight by a negative log of the normalized interimresult to produce the score.

The method continues at step 368 where the processing module rank ordersthe set of scores to produce ranked scoring information (e.g., ranking ahighest value first). The method continues at step 370 where theprocessing module outputs the ranked scoring information to therequesting entity. The requesting entity may utilize the ranked scoringinformation to select one location of a plurality of locations.

FIG. 4C is a schematic block diagram of an embodiment of a dispersedstorage network (DSN) that includes the distributed storage and task(DST) processing unit 16 of FIG. 1, the network 24 of FIG. 1, and thedistributed storage and task network (DSTN) module 22 of FIG. 1.Hereafter, the DSTN module 22 may be interchangeably referred to as aDSN memory. The DST processing unit 16 includes a decentralizedagreement module 380 and the DST client module 34 of FIG. 1. Thedecentralized agreement module 380 be implemented utilizing thedecentralized agreement module 350 of FIG. 5A. The DSTN module 22includes a plurality of DST execution (EX) unit pools 1-P. Each DSTexecution unit pool includes one or more sites 1-S. Each site includesone or more DST execution units 1-N. Each DST execution unit may beassociated with at least one pillar of N pillars associated with aninformation dispersal algorithm (IDA), where a data segment is dispersedstorage error encoded using the IDA to produce one or more sets ofencoded data slices, and where each set includes N encoded data slicesand like encoded data slices (e.g., slice 3's) of two or more sets ofencoded data slices are included in a common pillar (e.g., pillar 3).Each site may not include every pillar and a given pillar may beimplemented at more than one site. Each DST execution unit includes aplurality of memories 1-M. Each DST execution unit may be implementedutilizing the DST execution unit 36 of FIG. 1. Hereafter, a DSTexecution unit may be referred to interchangeably as a storage unit anda set of DST execution units may be interchangeably referred to as a setof storage units and/or as a storage unit set.

The DSN functions to receive data access requests 382, select resourcesof at least one DST execution unit pool for data access, utilize theselected DST execution unit pool for the data access, and issue a dataaccess response 392 based on the data access. The selection of theresources can include utilizing a decentralized agreement function ofthe decentralized agreement module 380, where a plurality of locationsare ranked against each other. The selection may also include selectingone storage pool of the plurality of storage pools, selecting DSTexecution units at various sites of the plurality of sites, selecting amemory of the plurality of memories for each DST execution unit, andselecting combinations of memories, DST execution units, sites, pillars,and/or storage pools.

In an example of operation, the DST client module 34 receives the dataaccess request 382 from a requesting entity, where the data accessrequest 382 includes a store data request, a retrieve data request, adelete data request, a data name, and/or a requesting entity identifier(ID). Having received the data access request 382, the DST client module34 determines a DSN address associated with the data access request. TheDSN address includes a source name (e.g., including a vault ID and anobject number associated with the data name), a data segment ID, a setof slice names, and/or a plurality of sets of slice names. Determiningthe DSN address can include generating the DSN address (e.g., for thestore data request) or retrieving the DSN address (e.g., from a DSNdirectory, from a dispersed hierarchical index) based on the data name(e.g., for the retrieve data request).

Having determined the DSN address, the DST client module 34 selects aplurality of resource levels (e.g., DST EX unit pool, site, DSTexecution unit, pillar, memory) associated with the DSTN module 22. Theselection of the resource levels may be based on the data name, therequesting entity ID, a predetermination, a lookup, a DSN performanceindicator, interpreting an error message and/or other information. Forexample, the DST client module 34 selects the DST execution unit pool asa first resource level and a set of memory devices of a plurality ofmemory devices as a second resource level based on a system registrylookup for a vault associated with the requesting entity.

Having selected the plurality of resource levels, the DST client module34, for each resource level, issues a ranked scoring information request384 to the decentralized agreement module 380 utilizing the DSN addressas an asset ID. The decentralized agreement module 380 performs thedecentralized agreement function based on the asset ID (e.g., the DSNaddress), identifiers of locations of the selected resource levels, andlocation weights of the locations to generate ranked scoring information386.

For each resource level, the DST client module 34 receives correspondingranked scoring information 386. Having received the ranked scoringinformation 386, the DST client module 34 identifies one or moreresources associated with the resource level based on the rank scoringinformation 386. For example, the DST client module 34 identifies a DSTexecution unit pool associated with a highest score and identifies a setof memory devices within DST execution units of the identified DSTexecution unit pool with a highest score.

Having identified the one or more resources, the DST client module 34accesses the DSTN module 22 based on the identified one or moreresources associated with each resource level. For example, the DSTclient module 34 issues resource access requests 388 (e.g., write slicerequests when storing data, read slice requests when recovering data) tothe identified DST execution unit pool, where the resource accessrequests 388 further identify the identified set of memory devices.Having accessed the DSTN module 22, the DST client module 34 receivesresource access responses 390 (e.g., write slice responses, read sliceresponses). The DST client module 34 issues the data access response 392based on the received resource access responses 390. For example, theDST client module 34 decodes received encoded data slices to reproducedata and generates the data access response 392 to include thereproduced data.

FIG. 4D is a flowchart illustrating an example of accessing a dispersedstorage network (DSN) memory. In step 394, a processing module (e.g., ofa distributed storage and task (DST) client module) receives a dataaccess request from a requesting entity. The data access request caninclude a storage request, a retrieval request, a requesting entityidentifier, a data identifier (ID) and/or other information. The methodcontinues at step 396 where the processing module determines a DSNaddress associated with the data access request. For example, theprocessing module generates the DSN address for the storage request. Asanother example, the processing module performs a lookup for theretrieval request based on the data identifier.

The method continues at step 398 where the processing module selects aplurality of resource levels associated with the DSN memory. Theselection, for example may be based on a predetermination, a range ofweights associated with available resources, a resource performancelevel, and/or a resource performance requirement level. For eachresource level, the method continues at step 400 where the processingmodule determines ranked scoring information. For example, theprocessing module issues a ranked scoring information request to adecentralized agreement module based on the DSN address and receivescorresponding ranked scoring information for the resource level, wherethe decentralized agreement module performs a decentralized agreementprotocol function on the DSN address using the associated resourceidentifiers and resource weights for the resource level to produce theranked scoring information for the resource level.

For each resource level, the method continues at step 402 where theprocessing module selects one or more resources associated with theresource level based on the ranked scoring information. For example, theprocessing module selects a resource associated with a highest scorewhen one resource is required. As another example, the processing moduleselects a plurality of resources associated with highest scores when aplurality of resources are required.

The method continues at step 404 where the processing module accessesthe DSN memory utilizing the selected one or more resources for each ofthe plurality of resource levels. For example, the processing moduleidentifies network addressing information based on the selectedresources including one or more of a storage unit Internet protocoladdress and a memory device identifier, generates a set of encoded dataslice access requests based on the data access request and the DSNaddress, and sends the set of encoded data slice access requests to theDSN memory utilizing the identified network addressing information.

The method continues at step 406 where the processing module issues adata access response to the requesting entity based on one or moreresource access responses from the DSN memory. For example, theprocessing module issues a data storage status indicator when storingdata. As another example, the processing module generates the dataaccess response to include recovered data when retrieving data.

FIG. 5 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a set of distributed storage andtask (DST) execution (EX) units 1-n, the network 24 of FIG. 1, and theDST processing unit 16 of FIG. 1. Each DST execution unit includes adecentralized agreement module, the DST client module 34 of FIG. 1, anda plurality of memories 1-M. The decentralized agreement module may beimplemented utilizing the decentralized agreement module 350 of FIG. 4A.Each memory may be implemented utilizing the memory 88 of FIG. 3. EachDST execution unit may be implemented utilizing the DST execution unit36 of FIG. 1. Hereafter, the set of DST execution units may beinterchangeably referred to as a set of storage units and each DSTexecution unit may be interchangeably referred to as a storage unit. TheDST processing unit 16 includes the DST client module 34 of FIG. 1. TheDSN functions to reallocate encoded data slice storage.

In many memory devices, there is some space reserved for handlinginternal errors that result in a decrease of that device's capacity. Forexample, Hard Drives with reserved bad blocks to take over from failedblocks elsewhere on the platter, or SSDs with whole redundant banks ofmemory. Normally, when this spare capacity is exhausted, the devicefails irrecoverably. As an alternative, if the usable capacity of thememory device were to merely continue to shrink as more and morefailures occurred, this would extend the usable life and utility of thecomponent, which is still non-zero, despite having less capacity. Yet ina DST execution unit of many memory devices, having some memory devicesof lesser capacity can be problematic: if one memory device reaches 100%before the others, then some writes to that DST execution unit will failwith out of space errors. To avoid this condition, while maintaining thecapacity to operate with degraded/reduced capacity memory devices, aDecentralized Agreement Protocol (DAP) may be used to effectively“shrink” the capacity of a memory device within a DST execution unit,such that utilization across all memory devices in a DST execution unitremains equal. By using a DAP to determine on which memory device tostore a slice, and by using a Resource Map that indicates the relativeremaining healthy storage capacities of each memory device, theplacement of slices can be weighted according to the storage capacity ofeach memory device. When a degradation occurs that causes the usablecapacity of a memory device to shrink, e.g. when a zone in an SMR drive,a platter or read head associated with a platter in a hard drive, a NANDarray in an SSD fails, or any similar fault, the DST execution unit maydetermine the new usable capacity of the memory device, then update theresource map, then reapply the DAP to the slices stored on that memorydevice. Since the device's capacity is reduced, the DAP will reallocatethose slices to other memory devices in the DST execution unit,eventually achieving equalized utilization

In an embodiment, a processing system of a dispersed storage and task(DST) execution unit comprises at least one processor and a memory thatstores operational instructions, that when executed by the at least oneprocessor causes the processing system to update a plurality ofweighting factors corresponding to each of a plurality of memories 1-Min response to an indication of a change in memory capacity of one ofthe plurality of memories. At least one encoded data slice is receivedfor storage by DST execution unit. A plurality of scores are generatedcorresponding to each of the plurality of memories, where each of theplurality of scores is based on one of the plurality of weightingfactors of a corresponding one of the plurality of memories. One of theplurality of memories is selected based on the plurality of scores, andthe at least one encoded data slice is stored in the selected one of theplurality of memories.

In various embodiments of the processing system of the DST executionunit, the indication of the change of the memory capacity is generatedbased on interpreting an error message.

In various embodiments of the processing system of the DST executionunit, execution of the operational instructions by the at least oneprocessor further causes the processing system to perform a memory teston the plurality of memories, and the indication of the change of memorycapacity is generated based on comparing a result of the memory test toa previous result corresponding to a previous memory test. Theindication of the change of the memory capacity can be generated basedon one of: a failure or removal of a memory block of the one of theplurality of memories. The plurality of weighting factors can be updatedin response to an indication that the change in memory capacity isgreater than a predefined threshold.

In various embodiments of the processing system of the DST executionunit, the selected one of the plurality of memories is determined basedon the corresponding one of the plurality of memories with the highestcorresponding score. Each of the plurality of scores can be furthergenerated based on an attribute of the received encoded slice.

The indication of a change in the memory capacity can indicate a loweredmemory capacity of the one of the plurality of memories, and theplurality of weighting factors can be updated by decreasing a weightingfactor of the plurality of weighting factors corresponding to the one ofthe plurality of memories with the lowered memory capacity, andincreasing remaining ones of the weighting factors of remaining memoriesof the plurality of memories.

In various embodiments of the processing system of the DST executionunit, the decrease of the weighting factor of the corresponding one ofthe plurality of memories with the lowered memory capacity can beproportional to a degree of change in the memory capacity, and remainingones of the plurality of weighting factors corresponding to theremaining memories are increased based on the decrease in the weightingfactor of the one of the plurality of memories with the lowered memorycapacity. The plurality of weighting factors can be updated in responseto an indication that at least one second encoded data slice has beeneither written to or deleted from the one of the plurality of memories.

In various embodiments, a storage unit determines that a memory capacitylevel of a memory of the plurality of memories of the storage unit haschanged. For example, the memory capacity level may change as a resultof a memory block failing, a memory block being taken out of service, oradding a memory block or other memory capacity to the memory. Thedetermining that the memory capacity level of a memory has changed mayinclude interpreting an error message, interpreting a memory testresult, comparing a test result to a previous test result and/oraccessing system registry information. For example, the DST executionunit 1 indicates that the memory capacity level of memory 2 of the DSTexecution unit 1 has changed when a memory block of the memory 2 hasfailed and has been taken out of service. The storage unit can alsoroutinely perform scheduled memory tests to determine if the memorycapacity has changed.

When the memory capacity level of the memory has changed, the storageunit updates weighting factors of the plurality of memories of thestorage unit in accordance with a degree of the change in the memorycapacity level. For example, when the memory capacity is lowered, theupdating can include lowering a weighting factor of the memory inaccordance with the degree of lowering, and raising weighting factors ofremaining memories (e.g., memory 1, memories 3-M) of the plurality ofmemories based on the lowering of the weighting factor in the memory(e.g., to proportionately spread out the overall weighting). In variousembodiments, the storage unit measures the change in memory capacity andonly updates weighting factors if the change in memory is greater than acertain threshold.

In various embodiments, the memory capacity level is determined based onthe remaining capacity of the memory. Determining that a memory capacitylevel of a memory has changed can also include determining if the amountof remaining, available memory has changed. For example, a change inmemory capacity can determined by an indication that a large file wasrecently added or removed from a memory block in the memory. In variousembodiments, the storage unit actively tracks and records when encodeddata slices are written or removed from memory, and can store a currentvalue, updated with each added or deleted slice, indicating the amountof remaining memory. In various embodiments, any addition or deletion ofa data slice will trigger the storage unit to update the weightingfactors accordingly. In other embodiments, the storage unit will monitorwhen data slices are written or removed and/or monitor a current valueindicating the amount of memory remaining, and only update the weighingfactors when the change in remaining memory capacity is above a certainthreshold, or when the number of data slices added or deleted is above acertain threshold.

In various embodiments, when the storage unit receives encoded dataslice for storage and receives a corresponding slice name, for eachmemory of the plurality of memories, the storage unit performs adistributed agreement protocol function on a slice name of the receivedencoded data slice using an identifier of the memory and an updatedweighting factor of the memory to produce a score of a plurality ofscores. The distributed agreement protocol function can be performed ona slice name, slice identifier, identifier corresponding to the sourceof the slice, and/or another identifier associated with the slice. Forexample, the DST processing unit 16 issues a set of encoded data slices1-n, the distributed agreement module of the DST execution unit 1performs the distributed agreement protocol function on a slice name ofreceived encoded data slice 1 using the identifier of the memory (e.g.,one at a time from 1-M) and the updated weighting factor of the memoryto produce the plurality of scores. In various embodiments, thedistributed agreement protocol function can be performed by thedecentralized agreement module 350 previously described and presented inFIG. 4A.

Having produced the plurality of scores, the storage unit facilitatesstorage of the received encoded data slice in a memory associated with ahighest score of the plurality of scores. For example, the DST clientmodule 34 of the DST execution unit 1 identifies highest score,identifies the memory (e.g., memory 6) associated with a highest score,and stores the received encoded data slice 1 in the identified memory.

FIG. 6 is a flowchart illustrating an example of reallocating encodeddata slice storage. In particular, a method is presented for use inconjunction with one or more functions and features described inconjunction with FIGS. 1-5 for execution by a dispersed storage and task(DST) execution unit that includes a processor or via another processingsystem of a dispersed storage network that includes at least oneprocessor and memory that stores instruction that configure theprocessor or processors to perform the steps described below. Step 602includes updating a plurality of weighting factors corresponding to eachof a plurality of memories in response to an indication of a change inmemory capacity of one of the plurality of memories. Step 604 includesreceiving at least one encoded data slice for storage by the DSTexecution unit. Step 606 includes generating a plurality of scorescorresponding to each of the plurality of memories, where each of theplurality of scores is based on one of the plurality of weightingfactors of a corresponding one of the plurality of memories. Step 608includes selecting one of the plurality of memories based on theplurality of scores. Step 610 includes storing the at least one encodeddata slice in the selected one of the plurality of memories.

In various embodiments, the indication of the change of the memorycapacity is generated based on interpreting an error message. The methodcan further include performing a memory test on the plurality ofmemories and, the indication of the change of memory capacity isgenerated based on comparing a result of the memory test to a previousresult corresponding to a previous memory test. The indication of thechange of the memory capacity can be generated based on one of: afailure or removal of a memory block of the one of the plurality ofmemories.

The plurality of weighting factors can be updated in response to anindication that the change in memory capacity is greater than apredefined threshold. The selected one of the plurality of memories canbe determined based on the corresponding one of the plurality ofmemories with the highest corresponding score. Each of the plurality ofscores can be further generated based on an attribute of the receivedencoded slice.

In various embodiments, the indication of a change in the memorycapacity indicates a lowered memory capacity of the one of the pluralityof memories, and the plurality of weighting factors can be updated bydecreasing a weighting factor of the plurality of weighting factorscorresponding to the one of the plurality of memories with the loweredmemory capacity, and increasing remaining ones of the weighting factorsof remaining memories of the plurality of memories. The decrease of theweighting factor of the corresponding one of the plurality of memorieswith the lowered memory capacity can be proportional to a degree ofchange in the memory capacity, and remaining ones of the plurality ofweighting factors corresponding to the remaining memories can beincreased based on the decrease in the weighting factor of the one ofthe plurality of memories with the lowered memory capacity. In addition,the plurality of weighting factors can be updated in response to anindication that at least one second encoded data slice has been eitherwritten to or deleted from the one of the plurality of memories.

In an embodiment, a non-transitory computer readable storage mediumcomprises at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to update a plurality of weighting factorscorresponding to each of a plurality of memories in response to anindication of a change in memory capacity of one of the plurality ofmemories, receive at least one encoded data slice for storage, generatea plurality of scores corresponding to each of the plurality ofmemories, where each of the plurality of scores is based on one of theplurality of weighting factors of a corresponding one of the pluralityof memories, select one of the plurality of memories based on theplurality of scores, and store the at least one encoded data slice inthe selected one of the plurality of memories.

The method described above in conjunction with the computing device andthe storage units can alternatively be performed by other modules of thedispersed storage network or by other devices. For example, anycombination of a first module, a second module, a third module, a fourthmodule, etc. of the computing device and the storage units may performthe method described above. In addition, at least one memory section(e.g., a first memory section, a second memory section, a third memorysection, a fourth memory section, a fifth memory section, a sixth memorysection, etc. of a non-transitory computer readable storage medium) thatstores operational instructions can, when executed by one or moreprocessing modules of one or more computing devices and/or by thestorage units of the dispersed storage network (DSN), cause the one ormore computing devices and/or the storage units to perform any or all ofthe method steps described above.

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “operably coupled to”, “coupled to”, and/or “coupling” includesdirect coupling between items and/or indirect coupling between items viaan intervening item (e.g., an item includes, but is not limited to, acomponent, an element, a circuit, and/or a module) where, for indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.As may even further be used herein, the term “operable to” or “operablycoupled to” indicates that an item includes one or more of powerconnections, input(s), output(s), etc., to perform, when activated, oneor more its corresponding functions and may further include inferredcoupling to one or more other items. As may still further be usedherein, the term “associated with”, includes direct and/or indirectcoupling of separate items and/or one item being embedded within anotheritem. As may be used herein, the term “compares favorably”, indicatesthat a comparison between two or more items, signals, etc., provides adesired relationship. For example, when the desired relationship is thatsignal 1 has a greater magnitude than signal 2, a favorable comparisonmay be achieved when the magnitude of signal 1 is greater than that ofsignal 2 or when the magnitude of signal 2 is less than that of signal1.

As may also be used herein, the terms “processing module”, “processingcircuit”, and/or “processing unit” may be a single processing device ora plurality of processing devices. Such a processing device may be amicroprocessor, micro-controller, digital signal processor,microcomputer, central processing unit, field programmable gate array,programmable logic device, state machine, logic circuitry, analogcircuitry, digital circuitry, and/or any device that manipulates signals(analog and/or digital) based on hard coding of the circuitry and/oroperational instructions. The processing module, module, processingcircuit, and/or processing unit may be, or further include, memoryand/or an integrated memory element, which may be a single memorydevice, a plurality of memory devices, and/or embedded circuitry ofanother processing module, module, processing circuit, and/or processingunit. Such a memory device may be a read-only memory, random accessmemory, volatile memory, non-volatile memory, static memory, dynamicmemory, flash memory, cache memory, and/or any device that storesdigital information. Note that if the processing module, module,processing circuit, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

The present invention has been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claimed invention. Further, theboundaries of these functional building blocks have been arbitrarilydefined for convenience of description. Alternate boundaries could bedefined as long as the certain significant functions are appropriatelyperformed. Similarly, flow diagram blocks may also have been arbitrarilydefined herein to illustrate certain significant functionality. To theextent used, the flow diagram block boundaries and sequence could havebeen defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claimed invention. One of average skill in the artwill also recognize that the functional building blocks, and otherillustrative blocks, modules and components herein, can be implementedas illustrated or by discrete components, application specificintegrated circuits, processors executing appropriate software and thelike or any combination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The present invention may have also been described, at least in part, interms of one or more embodiments. An embodiment of the present inventionis used herein to illustrate the present invention, an aspect thereof, afeature thereof, a concept thereof, and/or an example thereof. Aphysical embodiment of an apparatus, an article of manufacture, amachine, and/or of a process that embodies the present invention mayinclude one or more of the aspects, features, concepts, examples, etc.described with reference to one or more of the embodiments discussedherein. Further, from figure to figure, the embodiments may incorporatethe same or similarly named functions, steps, modules, etc. that may usethe same or different reference numbers and, as such, the functions,steps, modules, etc. may be the same or similar functions, steps,modules, etc. or different ones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of the various embodimentsof the present invention. A module includes a processing module, afunctional block, hardware, and/or software stored on memory forperforming one or more functions as may be described herein. Note that,if the module is implemented via hardware, the hardware may operateindependently and/or in conjunction software and/or firmware. As usedherein, a module may contain one or more sub-modules, each of which maybe one or more modules.

While particular combinations of various functions and features of thepresent invention have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent invention is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) execution unit that includes an interface and a processor,the method comprises: updating a plurality of weighting factorscorresponding to each of a plurality of memories of the DST executionunit in response to an indication of a change in memory capacity of oneof the plurality of memories and in accordance with a DecentralizedAgreement Protocol (DAP) that is implemented to maintain utilizationacross the plurality of memories to be substantially equal; receiving,via the interface and via a dispersed or distributed storage network(DSN) and from a DST processing unit, at least one encoded data slicefor storage by the DST execution unit; generating a plurality of scorescorresponding to each of the plurality of memories, wherein each of theplurality of scores is based on one of the plurality of weightingfactors of a corresponding one of the plurality of memories; selectingone of the plurality of memories based on the plurality of scores inaccordance with a resource map that indicates relative remaining healthystorage capacities of the plurality of memories; and storing the atleast one encoded data slice in the selected one of the plurality ofmemories.
 2. The method of claim 1, wherein the indication of the changeof the memory capacity is generated based on interpreting an errormessage.
 3. The method of claim 1, further comprising performing amemory test on the plurality of memories; and wherein the indication ofthe change of memory capacity is generated based on comparing a resultof the memory test to a previous result corresponding to a previousmemory test.
 4. The method of claim 1, wherein the indication of thechange of the memory capacity is generated based on one of: a failure orremoval of a memory block of the one of the plurality of memories. 5.The method of claim 1, wherein the plurality of weighting factors areupdated in response to an indication that the change in memory capacityis greater than a predefined threshold.
 6. The method of claim 1,wherein the selected one of the plurality of memories is determinedbased on the corresponding one of the plurality of memories with thehighest corresponding score.
 7. The method of claim 1, wherein each ofthe plurality of scores is further generated based on an attribute ofthe received encoded slice.
 8. The method of claim 1, wherein theindication of a change in the memory capacity indicates a lowered memorycapacity of the one of the plurality of memories, and wherein theplurality of weighting factors are updated by decreasing a weightingfactor of the plurality of weighting factors corresponding to the one ofthe plurality of memories with the lowered memory capacity, andincreasing remaining ones of the weighting factors of remaining memoriesof the plurality of memories.
 9. The method of claim 8, wherein thedecrease of the weighting factor of the corresponding one of theplurality of memories with the lowered memory capacity is proportionalto a degree of change in the memory capacity, and remaining ones of theplurality of weighting factors corresponding to the remaining memoriesare increased based on the decrease in the weighting factor of the oneof the plurality of memories with the lowered memory capacity.
 10. Themethod of claim 1, wherein the plurality of weighting factors areupdated in response to an indication that at least one second encodeddata slice has been one of: written to or deleted from the one of theplurality of memories.
 11. A processing system of a dispersed storageand task (DST) execution unit comprises: an interface configured tointerface and communicate with a dispersed storage network (DSN); atleast one processor; a memory that stores operational instructions, thatwhen executed by the at least one processor causes the processing systemto: update a plurality of weighting factors corresponding to each of aplurality of memories of the DST execution unit in response to anindication of a change in memory capacity of one of the plurality ofmemories and in accordance with a Decentralized Agreement Protocol (DAP)that is implemented to maintain utilization across the plurality ofmemories to be substantially equal; receive, via the interface and viathe DSN from a DST processing unit, at least one encoded data slice forstorage by the DST execution unit; generate a plurality of scorescorresponding to each of the plurality of memories, wherein each of theplurality of scores is based on one of the plurality of weightingfactors of a corresponding one of the plurality of memories; select oneof the plurality of memories based on the plurality of scores inaccordance with a resource map that indicates relative remaining healthystorage capacities of the plurality of memories; and store the at leastone encoded data slice in the selected one of the plurality of memories.12. The processing system of claim 11, wherein the indication of thechange of the memory capacity is generated based on interpreting anerror message.
 13. The processing system of claim 11, wherein executionof the operational instructions by the at least one processor furthercauses the processing system to perform a memory test on the pluralityof memories; and wherein the indication of the change of memory capacityis generated based on comparing a result of the memory test to aprevious result corresponding to a previous memory test.
 14. Theprocessing system of claim 11, wherein the indication of the change ofthe memory capacity is generated based on one of: a failure or removalof a memory block of the one of the plurality of memories.
 15. Theprocessing system of claim 11, wherein the plurality of weightingfactors are updated in response to an indication that the change inmemory capacity is greater than a predefined threshold.
 16. Theprocessing system of claim 11, wherein the selected one of the pluralityof memories is determined based on the corresponding one of theplurality of memories with the highest corresponding score.
 17. Theprocessing system of claim 11, wherein the indication of a change in thememory capacity indicates a lowered memory capacity of the one of theplurality of memories, and wherein the plurality of weighting factorsare updated by decreasing a weighting factor of the plurality ofweighting factors corresponding to the one of the plurality of memorieswith the lowered memory capacity, and increasing remaining ones of theweighting factors of remaining memories of the plurality of memories.18. The processing system of claim 17, wherein the decrease of theweighting factor of the corresponding one of the plurality of memorieswith the lowered memory capacity is proportional to a degree of changein the memory capacity, and remaining ones of the plurality of weightingfactors corresponding to the remaining memories are increased based onthe decrease in the weighting factor of the one of the plurality ofmemories with the lowered memory capacity.
 19. The processing system ofclaim 11, wherein the plurality of weighting factors are updated inresponse to an indication that at least one second encoded data slicehas been one of: written to or deleted from the one of the plurality ofmemories.
 20. A non-transitory computer readable storage mediumcomprises: at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to: update a plurality of weighting factorscorresponding to each of a plurality of memories of a dispersed storageand task (DST) execution unit in response to an indication of a changein memory capacity of one of the plurality of memories and in accordancewith a Decentralized Agreement Protocol (DAP) that is implemented tomaintain utilization across the plurality of memories to besubstantially equal; receive, via the DSN from a DST processing unit, atleast one encoded data slice for storage; generate a plurality of scorescorresponding to each of the plurality of memories, wherein each of theplurality of scores is based on one of the plurality of weightingfactors of a corresponding one of the plurality of memories; select oneof the plurality of memories based on the plurality of scores inaccordance with a resource map that indicates relative remaining healthystorage capacities of the plurality of memories; and store the at leastone encoded data slice in the selected one of the plurality of memories.